P1040: FACTORS ASSOCIATED WITH THROMBOSIS IN MYELOFIBROSIS
Authors: Aniket Bankar, Gopila Gupta, Verna Cheung, Jaime O. Claudio, Andrea Arruda, Hubert Tsui, Jose-Mario Capo-Chichi, Hassan Sibai, Marta Davidson, Dawn Maze, Vikas Gupta
Journal: HemaSphere (2023)

Topic: 16. Myeloproliferative neoplasms - Clinical Background: There is limited data on understanding of risk factors associated with thrombosis in patients with myelofibrosis (MF). Aims: To identify patient, disease and treatment related risk factors associated with thrombosis in myelofibrosis Methods:Design: Retrospective, single-centre study. Study population and setting: Consecutive patients with chronic phase MF (overt primary, secondary and pre-fibrotic) seen at Princess Margaret Cancer Centre, Toronto, Canada from 2004 to 2019 with follow up data updated through January 31, 2023 and identified from a prospective database (NCT02760238) Methodology: All variables were collected at the time of diagnosis/referral which in majority of cases was within 6 months of diagnosis. These included: age, sex, DIPSS risk category, blood counts (hemoglobin, WBC, platelets, blast percentage), driver mutations (JAK2, CALR, MPL), smoking, cardiovascular risk factors (obesity, hypertension, diabetes mellitus, hypercholesterolemia), history of prior thrombosis and palpable splenomegaly. Competing risk models were developed with death before occurrence of thrombosis as a competing event. Patients were censored at the time of last follow up date or transplant. An estimate of the effect of each variable on the incidence of thrombosis was obtained with Fine and Gray method as sub-distribution hazard ratios (SHR). Variables with p<0.10 in univariable analysis were entered into multivariable model. The final model was selected using stepwise backward selection using BICcr selection criteria. We then allocated points based on SHR to the factors significant (p<0.05) in multivariable model. The risk score was calculated for each patient in the dataset and then an internal validation was performed using 1000 times bootstrap resampling with this risk score as the predictor. Discrimination of the model was assessed using area under operating characteristics (AUC, also known as C-index) and Brier score. The calibration of the model was assessed graphically by comparing the predicted probability to the observed probability across 10 deciles of predicted risk. Results: Total 439 MF patients were included in the study, 229 (52%) were primary, 153 (35%), secondary, and 57 (13%) pre-fibrotic. The median age was 68.6 years (SD 12.7), 59% were male. The median follow-up in surviving patients was 6.9 years (IQR 5.1-10.1). There were 85 thrombotic events (58 venous, 27 arterial) during follow up. The univariate Fine Gray analysis showed that presence of JAK2 mutation, prior thrombosis, and female sex were associated with increased thrombosis risk and these variables also remained significant in the multivariable model (Figure1). Based on the SHR, risk scores were assigned as follows: 2 points for prior thrombosis (SHR 2.99, 95%CI 1.83-4.90) and 1 point each for JAK mutation (SHR 1.83, 95%CI 1.09-3.08) and female sex (SHR 1.61, 95%CI 1.05-2.47). Subsequent tallying of risk points allowed development of three-tiered risk model: Low risk (0 point), Intermediate risk (1-2 points) and High risk (3-4 points). Internal validation was performed using bootstrap with 1000 samples. The Wolber’s concordance index at 1-year was 0.65, suggesting adequate discrimination. The model showed good calibration graphically. Summary/Conclusion: SHR weight-based risk scoring can be used to stratify the risk of thrombosis in MF patients with reasonable discrimination. Further work is in progress to validate these findings in an additional independent dataset and updated data will be presented.Keywords: Myelofibrosis